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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Super-porous Pt/CuO/Pt hybrid platform for ultra-sensitive and selective H 2 O 2 detection

A super-porous Pt/CuO/Pt hybrid electrode is demonstrated with extremely high sensing performance parameters for the hydrogen peroxide (H 2 O 2 ) detection. A unique physiochemical approach is adapted for the fabrication of 3-D super-porous Pt/CuO/Pt sensing platform. Here, the super-porous Pt/CuO/Pt hybrid platform demonstrates a superior sensitivity of 16,694 µA mM -1 cm -2 with a limit of detection of 2.91 nM (S/N = 3). It also demonstrates an excellent selectivity against the interfering molecules such as NaCl, fructose, ascorbic acid, citric acid, dopamine and glucose with a wide linear range. The demonstrated performance ranks the Pt/CuO/Pt hybrid platform as one of the best H 2 O 2 sensors as summarized in the Table 2. The super-porous CuO layer is fabricated by a dynamic hydrogen bubbling technique of electrochemical deposition and metallic Pt NP decoration is achieved by the physical vapor deposition (PVD) and post-annealing. The super-porous CuO layer offers a drastically improved electrochemical active surface area, and the Pt NP decoration offers a significantly improved conductivity and improved charge accumulation for the H 2 O 2 reduction. The DFT simulations confirm the predominance of CuO over Cu 2 O and Pt over Pd for the H 2 O 2 detection based on the adsorption energy, density of states and charge accumulation calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Associative learning, a critical learning principle to improve an individual’s adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.

42 ENGINEERING↗
DOE OSTI2025

Copper-Based Two-Dimensional Conductive Metal–Organic Framework Thin Films for Ultrasensitive Detection of Perfluoroalkyls in Drinking Water

Perfluoroalkyls (PFAS) continue to emerge as a global health threat making their effective detection and capture extremely important. Though metal–organic frameworks (MOFs) have stood out as a promising class of porous materials for sensing PFAS, detection limits remain insufficient and a fundamental understanding of detection mechanisms warrants further investigation. Here, in this study, we show the use of a 2D conductive MOF film based on copper hexahydroxy triphenylene (Cu-HHTP) to fabricate chemiresistive sensing devices for detecting PFAS in drinking water. We further show ultrasensitive detection using electrochemical impedance spectroscopy. Owing to excellent electrostatic attractions and electrochemical interactions between the copper-based MOF and PFAS, confirmed by high-resolution spectroscopy and theoretical simulations, the MOF-based sensor reported herein exhibits excellent affinity and sensitivity toward perfluorinated acids at concentrations as low as 0.002 ng/L.

2D metal−organic frameworks↗
DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗
DOE OSTI2020

Nicotine Monitoring with a Wearable Sweat Band

The tobacco epidemic is a public health threat that has taken a heavy toll of lives around the globe each year. Smoking affects both the smokers and those who are exposed to secondhand smoke, and careful tracking of exposure can be key to mitigating the potential hazards. For smokers, the variation of chemical compositions between commercial cigarettes has led to ambiguity in estimating the health risks, both for active smokers and others involuntarily exposed to tobacco smoke and byproducts. In this regard, sweat possesses an attractive opportunity to monitor smoke exposure due to sweat's abundance in biomolecules and its great accessibility. In this paper, we present a wearable sweat band to monitor nicotine, a prominent ingredient in cigarettes, as a viable way to quantitatively assess a wearer's exposure to smoking. Both smokers and normal subjects are tested to demonstrate the use of this device for smoke-related health monitoring. Our results exhibit confirmable and elevated nicotine levels in sweat for subjects inhaling cigarette smoke. This continuous and personalized sweat sensing device is leverage to monitor smoke pollution for a potentially broad population.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Design, Construction, and Installation of In-Glovebox Pumped Actinide Molten Salt Loops

This report summarizes the work conducted in FY25 to construct and install pumped actinide-bearing molten salt loopsin a radiological glovebox. The establishment of such a capability will provide a platform in which technologies developed for Gen. IV molten salt reactors may be tested under relevant operating conditions. A review of convective molten salt systems is provided to frame Argonne’s pumped actinide loop relative to contemporary and historical systems. Moreover, the infrastructure needed to operate the system is detailed to provide additional context as to design metrics of the loops and supporting equipment. The pumps’ performance in water testing is outlined and used to inform operational activities planned in FY26. Finally, electrochemical experiments investigating fundamental reaction mechanisms governing reactive oxygen species that promote corrosion of molten salt media is also reported. In total, these combined loop installation and sensor development tasks will serve to aid in the future commercial deployment of molten salt reactors in the U.S.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Lithiating magneto-ionics in a rechargeable battery

Magneto-ionics, real-time ionic control of magnetism in solid-state materials, promise ultralow-power memory, computing, and ultralow-field sensor technologies. The real-time ion intercalation is also the key state-of-charge feature in rechargeable batteries. Here, we report that the reversible lithiation/delithiation in molecular magneto-ionic material, the cathode in a rechargeable lithium-ion battery, accurately monitors its real-time state of charge through a dynamic tunability of magnetic ordering. The electrochemical and magnetic studies confirm that the structural vacancy and hydrogen-bonding networks enable reversible lithiation and delithiation in the magnetic cathode. Coupling with microwave-excited spin wave at a low frequency (0.35 GHz) and a magnetic field of 100 Oe, we reveal a fast and reliable built-in magneto-ionic sensor monitoring state of charge in rechargeable batteries. The findings shown herein promise an integration of molecular magneto-ionic cathode and rechargeable batteries for real-time monitoring of state of charge.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Integration of Nuclear Material Accounting Data and Process Monitoring Data for Improvement on Detection Probability in Safeguarding Electrochemical Processing Facilities (Final Technical Report)

The KAERI advanced spent fuel conditioning process (ACP) process is a critical component of the US- South Korean nuclear cooperation and the following “123 Agreement.” Its development has received considerable attention in both countries. The ACP is an electrochemical processing (pyroprocessing) that recycles over 96% of the used nuclear fuel (UNF). It is also intrinsically proliferation-resistant in theory. In normal operation, the U/TRU product is very hot radiologically. In addition, the Cm provides a high level of spontaneous neutrons, making the product unsuitable for weapon use. However, as pointed in some study, “the need for safeguards to protect against the diversion and misuse of separated plutonium applies essentially equally to all grades of plutonium.” As pointed by many studies, the well-established traditional Nuclear Material Accounting (NMA) approach cannot be directly applied to electrochemical processing because of the lack of an input accountability tank, the non-continuous material flow, and the unsatisfactory level of confidence in sampling methods. Therefore, nuclear safeguards remain a grand challenge in the developing of commercial electrochemical separations facilities, especially around the heart of such facilities, the electrorefiner (ER) systems. In contrast to NMA data, process monitoring (PM) data is normally an indirect measurement of the SNM and is acquired much more frequently. In a broad sense, PM includes monitoring by various types of equipment, e.g. radiation detectors, cameras, voltage, current sensors. Because it is already being collected by the operator, the additional cost to safeguards is low. It has long been believed that PM data can supplement NMA data and help improve safeguards, although the benefits are hard to quantify. The U.S. DOE’s Material Protection, Accounting, and Control Technology (MPACT) campaign has made substantial investments into innovative PM sensor technology and predictive model development for real- or near real-time measurement and prediction of molten salt density and level, salt composition and actinide concentration especially Pu, the cell voltage, and the cell current to supplement traditional NMA. For aqueous-based reprocessing facilities, it is reported that PM, integrated with traditional NMA, have a high detection probability for specific diversions. For electrochemical reprocessing, preliminary studies have shown that PM data can support traditional NMA in various ways by providing a basis to estimate some of the in-processing nuclear material inventories. Despite early success, further studies on fusion of PM data and NMA data are still needed, which is the goal of this proposed work.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Hollow-core optical fibre sensors for operando Raman spectroscopy investigation of Li-ion battery liquid electrolytes

Improved analytical tools are urgently required to identify degradation and failure mechanisms in Li-ion batteries. However, understanding and ultimately avoiding these detrimental mechanisms requires continuous tracking of complex electrochemical processes in different battery components. Here, we report an operando spectroscopy method that enables monitoring the chemistry of a carbonate-based liquid electrolyte during electrochemical cycling in Li-ion batteries with a graphite anode and a LiNi 0.8 Mn 0.1 Co 0.1 O 2 cathode. By embedding a hollow-core optical fibre probe inside a lab-scale pouch cell, we demonstrate the effective evolution of the liquid electrolyte species by background-free Raman spectroscopy. The analysis of the spectroscopy measurements reveals changes in the ratio of carbonate solvents and electrolyte additives as a function of the cell voltage and show the potential to track the lithium-ion solvation dynamics. The proposed operando methodology contributes to understanding better the current Li-ion battery limitations and paves the way for studies of the degradation mechanisms in different electrochemical energy storage systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Microbial potentiometric sensor array measurements in unsaturated soils

The overarching goal of this study is to demonstrate a novel technology for monitoring changes in electrical potential of unsaturated soils using biofilm-populated electrodes. The novelty of the study stems from the fact that it demonstrates a method for measuring open-circuit potentials (OCP) in environments without the presence of an electrolyte solution. This study also reveals that using a biofilm-populated electrode as a reference in stable environments could successfully be employed to assess and monitor the electrochemical potential generated by plants and microorganisms. The findings imply that long-term (months to years) and real-time measurements of the open-circuit potential in unsaturated soils are possible. Because MPS arrays can directly measure open-circuit potential from the biofilm, the challenges related to locally induced electrochemical changes caused by microorganisms in the soil to achieve optimum physiological levels are eliminated. The simplicity of the technology, which allows for multiple indicator electrodes to be referenced against an “internal” reference electrode, enables spatial-temporal monitoring of the changes in the soil and the generation of 2D- or 3D-signal patterns. Once a signal pattern, generated by an array of sensors, develops (usually after 30 to 90 days), it does not significantly change unless the soil is exposed to external stimuli. The observed OCP phenomena, however, suggests that the change in OCP signal is independent of changes in soil conductivity resulting from the addition of water. In brief, findings suggest that the proposed technology can enable multidimensional profiling and long-term monitoring of changes occurring in unsaturated soils without direct implications of presence of water. The changes in the 2D or 3-D signal patterns, however, can be correlated to other important parameters that characterize soil health.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantitative Voltammetry Measurements of High-Concentration Actinides in Molten Chloride Fuel Salts

Molten chloride mixtures containing high concentrations of UCl 3 (i.e., >70 wt%) are candidates for next-generation molten salt reactor (MSR) fuel salts that enable new plant optimization and fuel cycle strategies. MSR licensing, however, is contingent on the development of effective material control and accounting (MC&A) practices for these salts. To close this gap, we have developed an electroanalytical approach that enables accurate in situ measurements of uranium concentrations in these highly loaded fuel salts. This methodology uses a multielectrode array voltammetry sensor combined with digital simulations that account for non-idealities such as uncompensated resistance effects. The theory-based numerical simulations of voltammetry responses yielded correction factors that are agnostic of electrochemical cell geometry and produced consistent voltammetry peak current results when correcting for varying amounts of ohmic resistance. In doing so, we have demonstrated accurate concentration measurements for UCl 3 in LiCl-KCl-UCl 3 across a range of mass fractions between 55 and 72.3 wt% at 550 °C.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Microbial spies and bloggers: programming cells to convert environmental information into discernible signals

Microbes regulate their dynamic behaviors using the chemical and physical characteristics of their environment. The ability of microbes to continuously convert this physicochemical information into biochemical information and to use organic matter in the environment as a power source makes these organisms attractive as chassis for building sensors. However, most biosensors have severe limitations when considering applications in hard-to-image settings like soils, sediments, and wastewater. Emerging technologies at the interface of biomolecular design, microbiome engineering, and synthetic biology offer new tools to program cells and communities as biosensors for these settings. Here, in this review, we describe innovations in biosensor outputs that are enabling new applications in complex environments, including reporters that are read out using electrochemical, gas chromatography, hyperspectral imaging, and next-generation sequencing methods. We also discuss computational advances that are accelerating the diversification of sensing components by mining metagenomics data for new transcriptional regulators and by designing allosteric protein switches that directly regulate reporter outputs using analytes. We highlight emerging opportunities for programming undomesticated microbes in communities to function as distributed sensors in the environment. Finally, we discuss the need for responsible biosensor development and to modernize regulatory frameworks to support evidence-based assessment of environmental biosensors.

analyte↗
DOE OSTI2024

Chemo-Mechanical Instabilities in Lithium Cobalt Oxide at Higher State-Of-Charge in Li-Ion Batteries

Transition metal oxide cathodes are widely used in commercial Li-ion batteries. However, their practical charge capacity is limited due to severe chemo-mechanical instabilities at higher charge voltage, and state-of-charge condition. Here, in situ stress and strain measurements were synchronized to probe mechanical deformations in the lithium cobalt oxide (LCO) cathode via a multi-beam stress sensor and digital image correlation, respectively. In situ mechanical measurements revealed how Li removal from the electrode structure induces deformations on the LCO composite cathodes during cycling. The structure and morphology of the LCO cathodes were further investigated by X-ray diffraction (XRD) and scanning electron microscopy (SEM) studies. Two distinct electrochemical and mechanical behaviors were identified when the LCO was charged up to 4.65 V. The LCO undergoes a compressive stress generation when charged up to 4.2 V and surface fractures on the LCO particles were detected by SEM. LCO cathode experienced significantly large contractions (negative strains) when charged up to 4.65 V, where intergranular crack formation and phase transformation were detected on the LCO particles via SEM and XRD, respectively. Overall, the study bridges complicated structural deformations with in situ analysis of mechanical degradations in LCO cathodes charged at higher voltages. The correlation is vital to understanding instability mechanisms in transition metal oxides at high voltages for alkali metal ion batteries.

higher state-of-charge↗
DOE OSTI2023

Real-Time Nitrate Ion Monitoring with Poly(3,4-ethylenedioxythiophene) (PEDOT) Materials

Nitrate (NO 3 ) pollution in groundwater, caused by various factors both natural and synthetic, contributes to the decline of human health and well-being. Current techniques used for nitrate detection include spectroscopic, electrochemical, chromatography, and capillary electrophoresis. It is highly desired to develop a simple cost-effective alternative to these complex methods for nitrate detection. Therefore, a real-time poly (3,4-ethylenedioxythiophene) (PEDOT)-based sensor for nitrate ion detection via electrical property change is introduced in this study. Vapor phase polymerization (VPP) is used to create a polymer thin film. Variations in specific parameters during the process are tested and compared to develop new insights into PEDOT sensitivity towards nitrate ions. Through this study, the optimal fabrication parameters that produce a sensor with the highest sensitivity toward nitrate ions are determined. With the optimized parameters, the electrical resistance response of the sensor to 1000 ppm nitrate solution is 41.79%. Furthermore, the sensors can detect nitrate ranging from 1 ppm to 1000 ppm. The proposed sensor demonstrates excellent potential to detect the overabundance of nitrate ions in aqueous solutions in real time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Memsensing by surface ion migration within Debye length

Integration between electronics and biology is often facilitated by iontronics, where ion migration in aqueous media governs sensing and memory. However, the Debye screening effect limits electric fields to the Debye length, the distance over which mobile ions screen electrostatic interactions, necessitating external voltages that constrain the operation speed and device design. Here we report a high-speed in-memory sensor based on vanadium dioxide (VO2) that operates without an external voltage by leveraging built-in electric fields within the Debye length. When VO2 contacts a low-work-function metal (for example, indium) in a salt solution, electrochemical reactions generate indium ions that migrate into the VO2 surface under the native electric field, inducing a surface insulator-to-metal phase transition of VO2. The VO2 conductance increase rate reflects the salt concentration, enabling in-memory sensing, or memsensing of the solution. The memsensor mimics Caenorhabditis elegans chemosensory plasticity to guide a miniature boat for adaptive chemotaxis, illustrating low-power aquatic neurorobotics with fewer memory units.

Guo, Ruihan↗